class_docstr: EfficientNet stage.
code: def complexity(cx, w_in, exp_r, k, stride, se_r, w_out, d):
      for _ in range(d):
          cx = MBConv.complexity(
              cx, w_in, exp_r, k, stride, se_r, w_out
          )
          stride, w_in = 1, w_out
      return cx
  
imports: from pycls.core.config import cfg
  from pycls.models.blocks import activation, conv2d, conv2d_cx, drop_connect, gap2d, gap2d_cx, init_weights, linear, linear_cx, norm2d, norm2d_cx, SE
  from torch.nn import Dropout, Module
label_desc: Use image node_id 3 for calling the EffStage obj's underlying member method code with example usage: obj.complexity(cx, w_in, exp_r, k, stride, se_r, w_out, d) and returns: cx